Experiment Dashboard - a generic, scalable solution for monitoring of the LHC computing activities, distributed sites and services

Andreeva, Julia; Cinquilli, M.; Dieguez, D; Dzhunov, I; Karavakis, E.; Karhula, Pekka; Kenyon, M.; Kokoszkiewicz, Lukasz; Nowotka, Michał; Rø, Gunnar; Sáiz, Pablo; Sargsyan, L; Schovancová, J.; Tuckett, David

doi:10.1088/1742-6596/396/3/032093

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…We focused on seven Tier2 sites, for which we knew the processor types present and the job history from information in the CMS Dashboard [49]. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Power-aware Applications for Scientific Cluster and Distributed

Elmer¹

2014

Proceedings of International Symposium on Grids and Clouds (ISGC) 2014 — PoS(ISGC2014)

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The aggregate power use of computing hardware is an important cost factor in scientific cluster and distributed computing systems. The Worldwide LHC Computing Grid (WLCG) is a major example of such a distributed computing system, used primarily for high throughput computing (HTC) applications. It has a computing capacity and power consumption rivaling that of the largest supercomputers. The computing capacity required from this system is also expected to grow over the next decade. Optimizing the power utilization and cost of such systems is thus of great interest. A number of trends currently underway will provide new opportunities for power-aware optimizations. We discuss how power-aware software applications and scheduling might be used to reduce power consumption, both as autonomous entities and as part of a (globally) distributed system. As concrete examples of computing centers we provide information on the large HEP-focused Tier-1 at FNAL, and the Tigress High Performance Computing Center at Princeton University, which provides HPC resources in a university context.

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“…We focused on seven Tier2 sites, for which we knew the processor types present and the job history from information in the CMS Dashboard [49]. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Power-aware Applications for Scientific Cluster and Distributed

Elmer¹

2014

Proceedings of International Symposium on Grids and Clouds (ISGC) 2014 — PoS(ISGC2014)

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show abstract

“…A simple simulation [48] allowed us to quantify the relation between possible performance improvements and energy savings in the WLCG under different scheduling policies. We focused on seven Tier2 sites, for which we knew the processor types present and the job history from information in the CMS Dashboard [49]. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Power-aware applications for scientific cluster and distributed computing

Abdurachmanov,

Elmer,

Eulisse

et al. 2014

Preprint

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The aggregate power use of computing hardware is an important cost factor in scientific cluster and distributed computing systems. The Worldwide LHC Computing Grid (WLCG) is a major example of such a distributed computing system, used primarily for high throughput computing (HTC) applications. It has a computing capacity and power consumption rivaling that of the largest supercomputers. The computing capacity required from this system is also expected to grow over the next decade. Optimizing the power utilization and cost of such systems is thus of great interest. A number of trends currently underway will provide new opportunities for power-aware optimizations. We discuss how power-aware software applications and scheduling might be used to reduce power consumption, both as autonomous entities and as part of a (globally) distributed system.As concrete examples of computing centers we provide information on the large HEP-focused Tier-1 at FNAL, and the Tigress High Performance Computing Center at Princeton University, which provides HPC resources in a university context.

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“…The ATLAS Distributed Computing project for LHC Run-2 and beyond Alessandro Di Girolamo Figure 2, taken from [4]-ATLAS concurrent running job slots. The red line represents the pledged resources.…”

Section: Pos(eps-hep2015)260mentioning

confidence: 99%

“…The EventIndex has been filled with all the Run 1 data and it is now running in parallel to the normal data taking and production activities, being filled as the data are produced. The Event Service [4] has been developed to optimize the usage of short-living resources, i.e. resources available at highly discounted prices, if agreed that they can be taken back by the provider with notice as short as a few minutes, or High Performance Computing resources, which can be used in backfill mode.…”

Section: Eventindex and Event Servicementioning

confidence: 99%

The ATLAS Distributed Computing project for LHC Run-2 and beyond.

Girolamo¹

2016

Proceedings of the European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2015)

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Experiment Dashboard - a generic, scalable solution for monitoring of the LHC computing activities, distributed sites and services

Cited by 10 publications

References 14 publications

Power-aware Applications for Scientific Cluster and Distributed

Power-aware Applications for Scientific Cluster and Distributed

Power-aware applications for scientific cluster and distributed computing

The ATLAS Distributed Computing project for LHC Run-2 and beyond.

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